Review of Multidetector Computed Tomography Angiography as a Screening Modality in the Assessment of Blunt Vascular Neck Injuries

AbstractBlunt vascular neck injuries (BVNI), previously thought to be rare, have demonstrated increasing incidence rates in recent literature and are associated with significant mortality and morbidity. A radiologist needs to efficiently recognize these injuries on preliminary screening to enable initiation of early management. When initiation of accurate management is started promptly, decreased rates of postinjury complications, for example, stroke, have been demonstrated. This article reviews the incidence, pathophysiology, and rationale for screening for these BVNI injuries. The utility of computed tomography angiography (CTA) as the potential new criterion standard as the screening and follow-up imaging modality for BVNI will be discussed. The application of new multidetector CTA techniques available, such as dual-energy CT and iterative reconstruction, are also reviewed. In addition, the characteristic imaging findings on CTA and the associated Denver Grading scale for BVNI will be reviewed to allow readers to become familiar with the injury patterns and to understand the prognostic and clinical implications, respectively. Examples of the spectrum of injuries, potential injury mimics, and different artifacts on multidetector CTA are shown to help familiarize readers and allow them to successfully and confidently recognize a true BVNI

A Novel Method for Radio Propagation Simulation Based on Automatic 3D Environment Reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target regions, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. Our proposition reduces, or even eliminates, infrastructure cost and human efforts during the construction of realistic 3D scenes used in radio propagation modeling. In addition, the results obtained from our propagation model proves to be both accurate and efficien

Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting

For data-driven building energy forecasting modeling, the quality of training data strongly affects a model’s accuracy and cost-effectiveness. In order to obtain high-quality training data within a short time period, experiment design, active learning, or excitation is becoming increasingly important, especially for nonlinear systems such as building energy systems. Experiment design and system excitation have been widely studied and applied in fields such as robotics and automobile industry for their model development. But these methods have hardly been applied for building energy modeling. This paper presents an overall discussion on the topic of applying system excitation for developing building energy forecasting models. For gray-box and white-box models, a model’s physical representations and theories can be applied to guide their training data collections. However, for black-box (pure-data-driven) models, the training data’s quality is sensitive to the model structure, leading to a fact that there is no universal theory for data training.  The focus of black-box modeling has traditionally been on how to represent a data set well. The impact of how such a data set represents the real system and how the quality of a training data set affect the performances of black-box models have not been well studied. In this paper, the system excitation method, which is used in system identification area, is used to excite zone temperature set-points to generate training data. These training data from system excitation are then used to train a variety of black-box building energy forecasting models. The models’ performances (accuracy and extendibility) are compared among different model structures. For the same model structure, its performances are also compared between when it is trained using typical building operational data and when it is trained using exited training data. Results show that the black-box models trained by normal operation data achieve better performance than that trained by excited training data but have worse model extendibility; Training data obtained from excitation will help to improve performances of system identification models

Low-power, high-speed FFT processor for MB-OFDM UWB application

This paper presents a low-power, high-speed 4-data-path 128-point mixed-radix (radix-2 & radix-2 2 ) FFT processor for MB-OFDM Ultra-WideBand (UWB) systems. The processor employs the single-path delay feedback (SDF) pipelined structure for the proposed algorithm, it uses substructure-sharing multiplication units and shift-add structure other than traditional complex multipliers. Furthermore, the word lengths are properly chosen, thus the hardware costs and power consumption of the proposed FFT processor are efficiently reduced. The proposed FFT processor is verified and synthesized by using 0.13 µm CMOS technology with a supply voltage of 1.32 V. The implementation results indicate that the proposed 128-point mixed-radix FFT architecture supports a throughput rate of 1Gsample/s with lower power consumption in comparison to existing 128-point FFT architecture

Radio propagation modeling and measurements for ZigBee based indoor wireless sensor networks

The deployment of nodes in Wireless Sensor Networks (WSNs) arises as one of the biggest challenges of this field, which involves in distributing a large number of embedded systems to fulfill a specific application. The connectivity of WSNs is difficult to estimate due to the irregularity of the physical environment and affects the WSN designers? decision on deploying sensor nodes. Therefore, in this paper, a new method is proposed to enhance the efficiency and accuracy on ZigBee propagation simulation in indoor environments. The method consists of two steps: automatic 3D indoor reconstruction and 3D ray-tracing based radio simulation. The automatic 3D indoor reconstruction employs unattended image classification algorithm and image vectorization algorithm to build the environment database accurately, which also significantly reduces time and efforts spent on non-radio propagation issue. The 3D ray tracing is developed by using kd-tree space division algorithm and a modified polar sweep algorithm, which accelerates the searching of rays over the entire space. Signal propagation model is proposed for the ray tracing engine by considering both the materials of obstacles and the impact of positions along the ray path of radio. Three different WSN deployments are realized in the indoor environment of an office and the results are verified to be accurate. Experimental results also indicate that the proposed method is efficient in pre-simulation strategy and 3D ray searching scheme and is suitable for different indoor environments

Improving target localization accuracy of wireless visual sensor networks

This paper discusses the target localization problem of wireless visual sensor networks. Specifically, each node with a low-resolution camera extracts multiple feature points to represent the target at the sensor node level. A statistical method of merging the position information of different sensor nodes to select the most correlated feature point pair at the base station is presented. This method releases the influence of the accuracy of target extraction on the accuracy of target localization in universal coordinate system. Simulations show that, compared with other relative approach, our proposed method can generate more desirable target localization's accuracy, and it has a better trade-off between camera node usage and localization accuracy

A Flexible Bayesian Model for Studying Gene–Environment Interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant

A Quantitative Assay for Insulin-expressing Colony-forming Progenitors

The field of pancreatic stem and progenitor cell biology has been hampered by a lack of in vitro functional and quantitative assays that allow for the analysis of the single cell. Analyses of single progenitors are of critical importance because they provide definitive ways to unequivocally demonstrate the lineage potential of individual progenitors. Although methods have been devised to generate "pancreatospheres" in suspension culture from single cells, several limitations exist. First, it is time-consuming to perform single cell deposition for a large number of cells, which in turn commands large volumes of culture media and space. Second, numeration of the resulting pancreatospheres is labor-intensive, especially when the frequency of the pancreatosphere-initiating progenitors is low. Third, the pancreatosphere assay is not an efficient method to allow both the proliferation and differentiation of pancreatic progenitors in the same culture well, restricting the usefulness of the assay

Sarcopenia is a Significant Predictor of Mortality After Abdominal Aortic Aneurysm Repair

Aims Repair of abdominal aortic aneurysms (AAA) decreases the incidence of rupture and death. In cancer patients, sarcopenia has been associated with increased surgical complications and mortality. The impact of sarcopenia on survival after AAA repair has yet to be described. Methods and Results Patient demographic, laboratory, body composition measurements and survival data were obtained from patients undergoing AAA repair at the Indiana University medical campus over a 5-year period. Univariate and multivariate analyses were performed to identify factors associated with overall survival. Overall, 58.2% presented with sarcopenia. Sarcopenic patients were older (71.8±8.3 versus 66.8±8.1 years; p<0.001), had lower body mass index (BMI) (26.3±5.2 versus 31.5±5.9 kg/m2; p<0.001), higher rates of myosteatosis (84.4% versus 52.%; p<0.001), greater AAA diameter (60.6±14.0 versus 57.8±11.7 mm; p=0.016), higher Charlson Comorbidity Index (CCI) (32.3% versus 25.1% ≥6; p=0.034), and increased rates of rupture (8.2% versus 3.8%; p=0.047). Sarcopenic and nonsarcopenic patients had no difference in 30-day morbidity (8.5% versus 8.5%; p=0.991) or mortality (3.7% versus 0.9%; p=0.07). Univariate analysis demonstrated age, sarcopenia, myosteatosis, CCI, and BMI to be associated with long-term survival. There was no correlation between BMI and sarcopenia. Both sarcopenia and myosteatosis resulted in decreased one-, three-, and five-year survivals compared to their counterparts. On multivariate analysis sarcopenia is independently associated with survival, conferring a 1.6-fold increase in death (p=0.04). The combination of sarcopenia plus myosteatosis doubled the risk of death compared to sarcopenia alone. Conclusions This is the first study to demonstrate that over half of all patients undergoing AAA repair are sarcopenic, a condition associated with increased mortality. Sarcopenia with myosteatosis is associated with double the mortality of sarcopenia alone. CT scan, but not BMI, accurately identifies sarcopenia and myosteatosis. Defining the mechanisms through which sarcopenia contributes to late death after AAA repair is critical to developing novel interventions that may improve survival in this high risk population